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arXiv · 2610.33292

KINESYS: Test-Time Adaptation for Reference-Based Effect Editing in Images

Noah Frahm·Luchao Qi·Haoze Li·Dan B Goldman·Sambit Bhattacharya·Roni Sengupta
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Abstract

Visual effects are often easier to demonstrate via reference than to describe in words. We study effect-reference editing, where a reference image shows a desired visual effect and language identifies which properties should guide the edit. Existing single-pass editing techniques often fail to make fine-grained edits, resulting in unintended changes to source content or deviations from the reference effect. We introduce KINESYS, a test-time adaptation framework for effect-reference editing that combines reference-aware and source-aware feedback to transfer the desired effect while preserving the composition and content of the original shot. To ensure KINESYS can produce a diverse set of edits that preserve the source, we develop a robust optimization strategy in which the generative model is fine-tuned across multiple noise samples and optimization runs. Evaluation with VLM judges, source preservation metrics (LPIPS, TPIPS), and a user study shows that KINESYS outperforms state-of-the-art image editors in source preservation; on effect transfer, it remains competitive with Nano Banana 2 and outperforms open-weight editors.

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Noah Frahm, Luchao Qi, Haoze Li, Dan B Goldman, Sambit Bhattacharya, Roni Sengupta. 2026-10-23. KINESYS: Test-Time Adaptation for Reference-Based Effect Editing in Images. https://arxiv.org/abs/2610.33292

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